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Conference

A Hybrid Kalman-Weighted Sliding Mode Observer for Sensorless Torque Estimation of Robotic Manipulators

Aug 2026 · 2026 IEEE International Conference on Mechatronics and Automation (ICMA) · pp. 669-674 · 0 citations · 18 references

Abstract

Accurate sensorless external force estimation is crucial for physical human-robot interaction. To address the challenge that existing momentum-based sliding mode observers face in simultaneously achieving fast dynamic response and effective chattering suppression, this paper proposes a Kalman-weighted adaptive second-order sliding mode observer (HKW-SOSMO). This method utilizes the discrete Riccati equation to compute the joint posterior covariance in real time, employing it as a dynamic weight to modulate the sliding mode switching gain. The gain is adaptively amplified in regions with sudden friction changes, while it decreases in steady-state regions as the covariance contracts. Based on Lyapunov theory, this paper proves the finite-time convergence of this variable-gain system. Simulation results demonstrate that the proposed method effectively resolves the trade-off between dynamic response and chattering, thereby significantly enhancing estimation accuracy.

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